Operations | Monitoring | ITSM | DevOps | Cloud

Visualize branches and commits, manage parallel work and agents, and run your entire Git workflow from one view. AI changed how code gets written. It also changed what developers spend their time doing. Today, developers are reviewing AI-generated changes, coordinating parallel work across branches and worktrees, cleaning up commit history, resolving conflicts, and getting everything ready to merge.

CFEngine 3.29.0 changes how storage promises treat a filesystem that is already mounted. A promise for an unmounted filesystem now mounts only that filesystem instead of all unmounted filesystems, edit_fstab actively maintains the file system table entry to keep it aligned with the promise even when the filesystem is already mounted, and an unmount promise acts only on the explicitly promised filesystem.

TL;DR Aiven contributed GCP support and private networking to the open source OpenSearch Migration Assistant, previously an AWS-only tool. Delivered via five Terraform-based PRs, the changes add a GKE deployment path and let regulated industries migrate without using the public internet - giving Elasticsearch, OpenSearch, and Solr users on GCP a supported route to Aiven for OpenSearch.

A single incident can generate the same alert several times in quick succession. These duplicate alerts create unnecessary noise and alert fatigue, and make it harder for on-call teams to focus on the issue that needs their attention. OnPage Alert Deduplication reduces repeat notifications while preserving visibility into every incoming alert.

Deployment is not the end of shipping your application. From time to time, you will get errors that you will need to attend to. Without a good method of catching errors or logs in general, you could end up with uncaught issues that might cost you valuable customers in the process. In this article, you will learn how to catch logs for an application deployed on Fly.io. You will learn how Fly.io logging works, then learn ways to handle logs natively on the platform.

Coralogix Real User Monitoring has spent years collecting full-fidelity user sessions: every session and event processed in stream, without sampling and without prior indexing, at rest in cloud object storage you own. Today that data does a second job. Product Analytics brings heatmaps, funnels, and pathways to the RUM sessions you already send, with no second SDK to install. One dataset now answers what your users did and why it happened.

The instinct when adopting AI coding agents is to optimize the agent. Compare models, tune prompts, argue about which editor has the better completion, and treat the agent as the thing that determines how fast the team moves. Then the commits go up and the product does not. The team building Upsun Dispatch took a different route, and the result is worth copying. They did not find a better agent.

If you’re like many Kubernetes users, you don’t pay much attention to where or how Kubernetes distributes your pods. As long as they’re running, it doesn’t matter where they get deployed, right? Surely Kubernetes will use some complex algorithm to figure out the most reliable way to distribute your pods across the cluster…right? Pod distribution plays a much bigger role in reliability than you might think.

An incident has hit your product. I've been there: you're context-switching between hosting, CI/CD, codebase, AppSignal for monitoring, and whatever else your product depends on to minimize downtime and potential losses. You're trying to piece everything together, but it takes a lot of time, and that's something you don't have. AI agents connected to your tooling and your monitoring data via MCP free up that time for you.